fix stuff from prof

main.typ

@@ -52,10 +52,10 @@
   title: "Few-Shot Learning for Anomaly Detection",
   abstract-en: [//max. 250 words
     This thesis explores the application of Few-Shot Learning (FSL) in anomaly detection, a critical area in industrial and automotive domains requiring robust and efficient algorithms for identifying defects.
-    Traditional methods, such as PatchCore and EfficientAD, achieve high accuracy but often demand extensive training data and are sensitive to environmental changes, necessitating frequent retraining.
+    Traditional methods for anomaly detection, such as PatchCore@patchcorepaper and EfficientAD@efficientADpaper, achieve high accuracy but often demand extensive training data and are sensitive to environmental changes, necessitating frequent retraining.
     FSL offers a promising alternative by enabling models to generalize effectively from minimal samples, thus reducing training time and adaptation overhead.
 
-    The study evaluates three FSL methods—ResNet50, P>M>F, and CAML—using the MVTec AD dataset.
+    The study evaluates three FSL methods—ResNet50@resnet, P>M>F@pmfpaper, and CAML@caml_paper—using the MVTec AD dataset.
     Experiments focus on tasks such as anomaly detection, class imbalance handling, //and comparison of distance metrics.
     and anomaly type classification.
     Results indicate that while FSL methods trail behind state-of-the-art algorithms in detecting anomalies, they excel in classifying anomaly types, showcasing potential in scenarios requiring detailed defect identification.